期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2014
卷号：2014
DOI：10.1155/2014/529842
出版社：Hindawi Publishing Corporation
摘要：In order to improve the quality of applications, such as recommendation or retrieval in knowledge-based service system, it is very helpful to uncover research topics of academic communities in scientific collaboration network (SCN). Previous research mainly focuses on network characteristics measurement and community evolution, but it remains largely understudied on how to uncover research topics of each community. This paper proposes a nonjoint approach, consisting of three simple steps: (1) to detect overlapping academic communities in SCN with the clique percolation method, (2) to discover underlying topics and research interests of each researcher with author-topic (AT) model, and (3) to label research topics of each community with top most frequent collaborative topics between members belonging to the community. Extensive experimental results on NIPS (neural information processing systems) dataset show that our simple procedure is feasible and efficient.
